• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.143-156
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• 2010

Faced with an increase of malicious threats from the Internet as well as local area networks, many companies are considering deploying a security system. To help a decision maker select a suitable security tool, this paper proposed a three-step integrated framework using linear fuzzy regression (LFR) and physical programming (PP). First, based on the experts' estimations on security criteria, analytic hierarchy process (AHP) and quality function deployment (QFD) are employed to specify an intermediate score for each criterion and the relationship among these criteria. Next, evaluation value of each criterion is computed by using LFR. Finally, a goal programming (GP) method is customized to obtain the most appropriate security tool for an organization, considering a tradeoff among the multi-objectives associated with quality, credibility and costs, utilizing the relative weights calculated by the physical programming weights (PPW) algorithm. A numerical example provided illustrates the advantages and contributions of this approach. Proposed approach is anticipated to help a decision maker select a suitable security tool by taking advantage of experts' experience, with noises eliminated, as well as the accuracy of mathematical optimization methods.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.608-609
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• 2011

전력수요 예측 오차가 큰 추석 연휴 및 전, 후일 전력수요 예측의 정확성을 향상시키기 위해 과거 추석 연휴 및 전, 후일에 대한 전력수요 특성을 분석하고 최대/최소 전력 예측을 위한 퍼지 입력데이터 선정 방법과 24시간 예측을 위한 정규화에 필요한 입력 데이터 선정방법을 개발하여 퍼지 선형회귀분석 모델을 사용하여 2006년에서 2010년까지 5개년의 사례연구를 통해 알고리즘의 우수성을 검증하였다.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.81-90
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• 2006

It is important to clarify the relationship between traffic accidents and various influencing factors in order to reduce the number of traffic accidents. This study developed a traffic accident frequency prediction model using by multi-linear regression and qualification theories which are commonly applied in the field of traffic safety to verify the influences of various factors into the traffic accident frequency The data were collected on the Korean National Highway 17 which shows the highest accident frequencies and fatality rates in Chonbuk province. In order to minimize the uncertainty of the data, the fuzzy theory and neural network theory were applied. The neural network theory can provide fair learning performance by modeling the human neural system mathematically. Tn conclusion, this study focused on the practicability of the fuzzy reasoning theory and the neural network theory for traffic safety analysis.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.1-10
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• 2009

Wooded areas that are a part of school campuses are one type of urban forest. Most schools located in an urban environment make an excellent setting for a forest in terms of location and area. These kinds of wooded spaces also make the city greener and healthier. As a place where students spend a great deal of time, schools can also be a venue for environmental education. The creation of wooded areas in schools currently has focused on the end result only; by ignoring student needs and participation, these areas have not had a significant influence on student environmental education. Previous studies based on questionnaire survey are significant in that they have quantified subjective qualitative data via Likert Scale. There has been, however, a problem in quantifying the more ambiguous subjective data. Therefore, this paper has attempted to investigate those factors that have an influence on student satisfaction with the wooded areas of their school campus using Fuzzy Theory with elementary school students in Gyeongsangbuk-do. A change was observed in terms of the ranking of arithmetic mean values of 'school peculiarity' and 'emotion evolution' and center of gravity, which has adopted Fuzzy Theory, proving that Fuzzy Theory could rationally objectify qualitative data such as human thoughts. In terms of the influential factors on the satisfaction with school forests(regression coefficient), 'school uniqueness(0.159)' was the highest, followed by 'many trees(0.142),' 'importance of nature(0.136)' and 'emotion evolution(0.130).' This paper may therefore be useful as basic data for objective questionnaire surveys and the development of school forests.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.547-565
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• 2011

Different types of schemes have been used in stage prediction involving conceptual and physical models. Nevertheless, none of these schemes can be considered as a single superior model. To overcome disadvantages of existing physics based rainfall-runoff models for stage predicting because of the complexity of the hydrological process, recently the data-derived models has been widely adopted for predicting flood stage. The objective of this study is to evaluate model performance for stage prediction of the Neuro-Fuzzy and regression analysis stage prediction models in these data-derived methods. The proposed models are applied to the Wangsukcheon in Han river watershed. To evaluate the performance of the proposed models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient(NSEC), mean absolute error(MAE), adjusted coefficient of determination($R^{*2}$). The results show that the Neuro-Fuzzy stage prediction model can carry out the river flood stage prediction more accurately than the regression analysis stage prediction model. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Journal of Korean Society of Steel Construction
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• v.9 no.4 s.33
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• pp.579-588
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• 1997

During the construction of cable stayed bridge, errors are always caused by various reasons, accumulated and amplified through the complex construction steps. It is likely that the undesirable stress distribution of members and the large deflection of the bridge different from design values come out The adjustment of cables during construction is absolutely indispensable to correct the stress distribution of the members and the geometrical configuration of the bridge. In the conventional method, weight coefficients are used to consider the difference of units between cable forces and girder deflections during the optimization process of cable adjustment. However, it is not easy to determine weight coefficients and the adjustment must be repeated several times with the time consuming process of the determination of new weight coefficients in case that errors are out of design allowable limits. In this paper, fuzzy linear regression analysis is applied to the cable adjustment to overcome those problems. In the application of fuzzy linear regression analysis method the designer's intention and the design allowable limits can be formulated in the form of the constraints of the linear optimization problem. Therefore, the cable adjustment in construction site can be carried out with the fuzzy linear regression analysis more rapidly than with the convetional method.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.371-383
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• 1997

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, a method of linear fuzzy regression analysis is described by interpreting the reliability of each input-output pair as its membership values. Next, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. The fuzzy neural network maps a crisp input vector to a fuzzy output. A cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is illustrated by computer simulations on numerical examples.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2768-2770
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• 2001

본 논문은 지능형 알고리즘과 이미지 프로세싱 방법을 결합한 새로운 방법으로 칼라 매칭 시스템에 구현한다. 칼라 매칭 시스템은 이미지 프로세싱을 이용하여 칼라의 RGB 데이터를 분석한 후 얻어진 색상정보를 가지고 사용자가 원하는 칼라는 구현하는 시스템이다. 칼라 매칭 시스템의 모델링에 이용되는 지능형 모델은 퍼지 추론과 적응 퍼지 추론 시스템(Adaptive Neuro-Fuzzy Inference System: ANFIS)이며, 최소 자승법을 기반으로 한 회귀 다항식과 비교하여 제안된 지능형 모델에 대한 성능과 실용성을 검증한 후 델파이를 이용하여 구현하였다.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.377-388
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• 2001

Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach restricted within a full understanding of every interrelationships among the parameters. This paper use fuzzy logic system that is capable of uniformly approximating any nonlinear function and applying cognitive psychology theory. First of all, we extract multiple regression equation from the factors of 12 software complexity metrics collected from Java programs. We apply cognitive psychology theory in program volume factor, and then measure program volume complexity to execute fuzzy learning. This approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics.